financialriskmeter

 

Current Risk level:

 

 

low risk of crisis

Green: low risk of crisis in the financial market.
The incidence of a crisis is less likely than usual.
Current risk level since 22/04/2019.

 

What is this about:

The Financial Risk Meter (FRM) helps you to identify different systemic risk level in the financial market over time. It is an index of the system volatility level which indicates that if FRM is high, then the systemic risk is high.

Risk level explained:

The risks are ordered into different levels with a different color code each. ✗ denotes the current risk level.

 
 

 

severe risk

Red: severe risk of a crisis in the financial market. Our risk measure suggests that a financial crisis is imminent or happening right now. This risk level is given for lambda values higher than the 80%-ratio.

 

high risk

Orange: high risk of crisis in the financial market. A crisis might occur very soon. This risk level is given for lambda values between the 60%-ratio and 80%-ratio.

 

elevated risk

Yellow: elevated risk of crisis in the financial market. The incidence of a crisis is somewhat higher than usual. This risk level is given for lambda values between the 40%-ratio and 60%-ratio .

 

general risk

Blue: general risk of crisis in the financial market. There is no specific risk of a crisis. This risk level is given for lambda values between the 20%-ratio and 40%-ratio.

 

low risk

Green: low risk of crisis in the financial market. The incidence of a crisis is less likely than usual. This risk level is given for lambda values lower then the 20%-ratio.
Current risk level since 22/04/2019: 16.29%-ratio

 

Timeline

Evolution of risk over time. In 2007 the subprime mortgage crisis started. In 2008 the global financial crisis swept the world, the European sovereign debt crisis broke out in the same year. After 2013 the global economy is showing signs of the slow recovery from the recession. High or low levels of systemic risk play different roles in each period. You may get a clue or some evidence of these financial events by following the timeline and its corresponding volatility level in this interactive chart, on which you can observe how the systemic risk evolved over time by exploring the historical data of 200 US financial firms.

 

Interactive moving time window: select desired frame in lower graph.

 

Details

We propose a linear lasso measure to estimate systemic interconnectedness across financial institutions based on tail-driven spill-over effects in an ultra-high dimensional framework. Methodologically, we employ a variable selection technique in a time series setting for a linear quantile regression framework with 5% quantile. We can thus include more financial institutions into the analysis, to measure their interdependencies in tails.

Then FRM is induced from this model which is the averaged tuning parameter lambda from lasso technique. The estimation method of it is cross validation. In application we apply 100 US publicly traded financial institutions and 6 macro state variables to estimate this index. Previously we have used 200 financial institutions, after comparison we find out that using 100 firms is more efficient way.

 

References

Ideas, papers, theory and code used in this project:

 

 

Single-Index-Based CoVaR With Very High-Dimensional Covariates (2017)
Journal of Business&Economic Statistics
Yan Fan, Wolfgang Karl Härdle, Weining Wang and Lixing Zhu

Get Document

 

TENET: Tail-Event driven NETwork risk (2016)
Journal of Econometrics
Wolfgang Karl Härdle, Weining Wang, Lining Yu

Get Document

 

LASSO-Driven Inference in Time and Space (2018)
IRTG 1792 Discussion Paper 2018-021
Victor Chernoyhukov, Wolfgang Karl Härdle, Chen Huang, Weining Wang

Get Document

 

FRM: A Financial Risk Meter Based on Penalizing Tail Events Occurrence (2017)
SFB 649 Discussion Paper 2017-003
Lining Yu, Wolfgang Karl Härdle, Lukas Borke, Thijs Benshop

Get Document

 

QuantNet
QuantNet is designed as a web-interface to freely exchange numerical methods, called Quantlets

 

R code for FRM
Search for FRM in QuantNet

 

Who we are

 

Theoretical framework,
Quantile Lasso Regression algorithms,
scientific advice

Weining Wang

Hermann-Otto-Hirschfeld Professor,
Humboldt-Universität zu Berlin

Theoretical framework,
Quantile Lasso Regression algorithms,
scientific advice

Xinjue Li

PhD student,
Xiamen University

Theoretical core driver,
scientific advice

Cathy Yi-hsuan Chen

Professor Ladislaus von Bortkiewicz Lehrstuhl für Statistik, Humboldt-Universität zu Berlin

Theoretical framework,
Quantile Lasso Regression algorithms,
scientific advice

Michael Althof

PhD student,
Humboldt-Universität zu Berlin

Theoretical core driver,
scientific advice

Wolfgang Karl Härdle

Professor Ladislaus von Bortkiewicz Lehrstuhl für Statistik, Humboldt-Universität zu Berlin

Theoretical core driver,
Research training, development of algorithms and
scientific advice

Andrija Mihoci

Professor and Appointed Lecturer in Statistics, Econometrics and Quantitative Methods,
Brandenburg University of Technology Cottbus-Senftenberg

 

Impressum

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